A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 Dynamic Software Architectures Verification using DynAlloy Antonio Bucchiarone IMT Graduate School of Lucca, Italy and ISTI-CNR of Pisa, Italy and Juan P. Galeotti Universidad de Buenos Aires, Argentina

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 Agenda  Introduction  Background  QoS Model for Service Composition  A Front-End Application  Queueing Model  Service Composition Flow Models  QoS Service Composition Algorithm  Related Work  Conclusions and Future Work

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 Introduction  SOC is a promising means to integrate heterogeneous systems  Services from different providers can be integrated into a composite service  QoS of Data-Intensive applications  Reliability  Performance  Service description  QoS dynamic composition

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 Background - I  Services (functionality)  Context adaptive and intelligent user services  Information services  Intermediary services  Location-based services  Services (technical protocols)  Web Services  Grid Services  “a service provides some useful functionality through a well defined interface and it is possible to combine them to produce useful “composite” services.”

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 Background - II  Services Composition  Web Services  SOAP, WSDL and UDDI  Orchestration (BPEL4WS) and Choreography (WS-CDL)  Grid Services  To utilize the power of heterogeneous distribute resources, computing resources, data storage systems, instruments,..  Grid and Web Services are converging in the WSRF  A series of specifications for performing grid computing on top of web services

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 Background - III  Service Level Agreement (SLA)  It complements a service description language  It defines the agreed performance characteristics and the way to evaluate and measure them

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 QoS Model for Service Composition  Front-End application  The actors and theirs main functionalities  Queueing Model  For data-intensive applications  Services Composition Flow Models  Types of services and basic relationships  QoS parameters  QoS Service Composition Algorithm  To compose services that have QoS attributes

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 QoS Model for Service Composition Front-End application - I  “A system that is able to put together different services from various providers in order to construct and execute a data-intensive application”  Providers  Companies distributed on the net  Customer  Client that wants execute a new application that satisfies some QoS characteristics  Front-End application  System that realizes the application

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 QoS Model for Service Composition Front-End application - II  Service Description (providers)  QoS parameters for the service classification  Service Classification (front-end)  It subdivides the services in classes based on their description and QoS attributes  Workflow definition (front-end)  Services composition structure  Meta-workflow (data-flow, Pipeline)  Application Chosen (Customer)  It choose the kind of application that he wants (data mining, astronomy, traveling, tourism,..)  QoS Inizialization (Customer)  max-cost of the service composition  min-perf : medium departure time of each result in the last node of the workflow  QoS Algorithm (front-end)  QoS Services Composition  Input: Workflow definition and QoS parameters  Output: workflow in which the services are instanciated (final application)  Searching Services (front-end)  Search a service based on QoS parameters  Execution  Throw Exception

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 QoS Model for Service Composition Queueing Model  From: L. Kleinrock, “Queueing Systems, Vol. I: Theory,”  The arrival process of customers ( T a )  The service times ( T s )  The service discipline  Fifo  Lifo  Random  Processor sharing  The queue occupation rate or server utilization  ρ = T s / T a  Our model: Data-Flow  Our target: ρ < 1

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 QoS Model for Service Composition Services Composition Flow Models - I  Types of Services  Relationships (DAG)  Data-flow  Or  Pipeline

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 QoS Model for Service Composition Services Composition Flow Models - II  Goal: build an application from an automatic composition of services  QoS parameters  Intuitive  Easy to measure

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 QoS Model for Service Composition QoS Service Composition Algorithm  Input  Workflow definition of the service composition, and QoS attributes  Output  Workflow instantiated with real services satisfying QoS attributes

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 Related Work  SWORD project  Rule-based Web Services composition  Offline composition (not at run time)  FUSION framework  Web Services composition according users satisfaction criteria  Run time composition  WebQ framework  Adaptive management of Web services  QoS selection criterion only considers service load and makes only local decisions  EFlow  A platform for the specification, enactment and management of composite services (graphs)  The graphs may include service, decision and event nodes  GSFL (Grid Service Flow Language)  Grid Services composition

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 Conclusions  A front-end application in order to develop data- intensive applications starting from services developed by different owners  QoS composition algorithm  Open Points  To enrich composition model with more complex workflows  To enrich QoS composition algorithm considering the transmission time parameter that can be variable  To implement the front-end application with the QoS composition algorithm in order to use it in a real case-study

A. Bucchiarone, Juan P. Galeotti / GT-VMT’08 Thank you for your attention! Antonio Bucchiarone PhD Student – IMT Graduate School Piazza S. Ponziano 6, Lucca (Italy) and Luigi Presti IBM Software Group – Tivoli Rome Lab Via Sciangai 53, Rome (Italy)